Research of neural network algorithm based on factor analysis and cluster analysis

Aiming at the large sample with high feature dimension, this paper proposes a back-propagation (BP) neural network algorithm based on factor analysis (FA) and cluster analysis (CA), which is combined with the principles of FA and CA, and the architecture of BP neural network. The new algorithm reduces the feature dimensionality of the initial data through FA to simplify the network architecture; then divides the samples into different sub-categories through CA, trains the network so as to improve the adaptability of the network. In application, it is first to classify the new samples, then using the corresponding network to predict. By an experiment, the new algorithm is significantly improved at the aspect of its prediction precision. In order to test and verify the validity of the new algorithm, we compare it with BP algorithms based on FA and CA.

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